Research Scientist, Recommendation Systems

Role overview Develop recommendation systems that personalize user experiences at scale. The work combines applied research with practical model development, using behavioral and user-generated data to improve ranking, personalization, and discovery. Findings are expected to move from research into production through close partnership with product and engineering teams.

Responsibilities - Design, implement, and evaluate machine-learning models for recommendation, ranking, and personalization. - Research ways to improve model quality, robustness, and efficiency. - Plan experiments using offline evaluation and online testing to assess system changes. - Analyze complex datasets to understand user behavior and recommendation performance. - Work with engineering and product teams to transition research prototypes into production systems. - Explain results clearly to technical and non-technical audiences and stay current with relevant research.

Requirements - Advanced degree in computer science, machine learning, statistics, or a related field, or equivalent experience. - Experience developing recommendation, ranking, or personalization models for user-generated content. - Strong foundations in machine learning, statistics, and data analysis. - Proficiency in Python and a modern machine-learning framework such as PyTorch. - Ability to conduct independent research and collaborate effectively with a team.

Nice to have Research publications or submissions in recommendation systems; experience with large-scale data and computing, representation learning, sequence models, large-scale optimization, or productionizing research prototypes.

Benefits and work setup The listing describes influence over research and product direction, collaboration across research, engineering, and product, support for professional growth, and competitive compensation and benefits.